fix & test: added lattice conversion tests and fixes to make it pass

matsciml/datasets/schema.py

@@ -803,7 +803,7 @@ def coordinate_consistency(self) -> Self:
                         "Fractional coordinate dimensions do not match cartesians."
                     )
                 )
-            if min(self.frac_coords) < 0.0 or max(self.frac_coords) > 1.0:
+            if self.frac_coords.min() < 0.0 or self.frac_coords.max() > 1.0:
                 self._exception_wrapper(
                     ValueError("Fractional coordinates are outside of [0, 1].")
                 )

matsciml/datasets/tests/test_schema.py

@@ -4,6 +4,7 @@
 import pytest
 import numpy as np
 import torch
+from ase.geometry import cell_to_cellpar
 
 from matsciml.datasets import schema
 from matsciml.datasets.transforms import PeriodicPropertiesTransform
@@ -175,3 +176,24 @@ def test_data_sample_fail_coord_shape(num_atoms, array_lib):
             pbc=pbc,
             datatype="SCFCycle",
         )
+
+
+def test_lattice_param_to_matrix_consistency():
+    """Make sure that lattice parameters map to matrix correctly during validation"""
+    coords = np.random.rand(5, 3)
+    numbers = np.random.randint(1, 100, (5))
+    data = schema.DataSampleSchema(
+        index=0,
+        num_atoms=5,
+        cart_coords=coords,
+        atomic_numbers=numbers,
+        pbc={"x": True, "y": True, "z": True},
+        datatype="OptimizationCycle",
+        lattice_parameters=[5.0, 5.0, 5.0, 90.0, 90.0, 90.0],
+    )
+    assert data.frac_coords is not None
+    assert data.lattice_matrix is not None
+    reconverted = cell_to_cellpar(data.lattice_matrix)
+    assert np.allclose(reconverted, data.lattice_parameters)
+    exact = np.array([[5.0, 0.0, 0.0], [0.0, 5.0, 0.0], [0.0, 0.0, 5.0]])
+    assert np.allclose(exact, data.lattice_matrix)

matsciml/datasets/utils.py

@@ -964,7 +964,7 @@ def csc(x: float) -> float:
 
     # This matrix is normally for fractional to cart. Implements the matrix found in
     # https://en.wikipedia.org/wiki/Fractional_coordinates#General_transformations_between_fractional_and_Cartesian_coordinates
-    rotation = torch.tensor(
+    rotation = np.array(
         [
             [
                 a
@@ -987,11 +987,13 @@ def csc(x: float) -> float:
             ],
             [a * np.cos(beta), b * np.cos(alpha), c],
         ],
-        dtype=coords.dtype,
     )
     if to_fractional:
         # invert elements for the opposite conversion
-        rotation = torch.linalg.inv(rotation)
+        rotation = np.linalg.inv(rotation)
+    # if coords are already torch, cast as a tensor so we can matmul
+    if isinstance(coords, torch.Tensor):
+        rotation = torch.from_numpy(rotation).to(coords.type)
     output = coords @ rotation
     return output